Emission control devices, such as particulate filters (PF), may reduce the amount of soot emissions from an internal combustion engine by trapping soot particles. Such devices may be passively regenerated during operation of the engine to decrease the amount of trapped particulate matter. However, during vehicle operation, conditions for sustained full regeneration of the PF may not be available. For example, during urban driving conditions which include frequent idle stops and light load engine operation, frequent premature terminations of regeneration may occur. Premature terminations of regeneration may occur due to the driving behavior, such as frequent brake application, of the vehicle driver (herein also referenced to as the operator). The premature terminations may result in the need for active regeneration, leading to an increased regeneration fuel penalty.
Various approaches are provided for regenerating a PF during a vehicle drive cycle. In one example, as shown in U.S. Pat. No. 8,424,294, Schumacher et al. disclose a method to control the regeneration cycles of an exhaust gas after treatment system, such as a particulate filter, based on driver specific information such that an optimal regeneration is achieved. The driver specific information may include driving habits, driving cycles, and driving routes used by the driver. Such driver specific information may be utilized to predict phases during a drive when regeneration of the particulate filter may be possible.
However, the inventors herein have recognized potential disadvantages with the above approach. As one example, driver specific information may not remain constant during a drive cycle resulting in a significant difference between a predicted driver behavior along a travel route and a real-time driver behavior. As a result, a route planned based on the predicted driver behavior, without accounting for temporal changes in driver behavior, may have a PF regeneration efficiency that is different from the actual PF regeneration efficiency. Also, environmental factors including traffic conditions and weather conditions may significantly affect the possibility of completion of regeneration over the drive cycle. Further, selection of a travel route solely based on driver preferences may result in higher fuel consumption and increased duration of travel.
In one example, the issues described above may be addressed by an engine method, comprising: at an onset of a drive cycle, displaying a first driving route responsive to each of a particulate filter (PF) loading and past driving history; and during travel along the first driving route, displaying an updated route responsive to each of traffic conditions and a comparison of a driving history along the first route on the drive cycle relative to the past driving history. In this way, by estimating a driver state of mind in real-time and quantitatively using the driver state of mind to recommend routes to a vehicle driver, PF regeneration efficiency may be improved.
As one example, a vehicle controller may develop a route database for a vehicle driver as a function of routes that are frequently used, along with drive history on each route. Each time a trip is completed, the database may be updated with information regarding driver characteristics including driving practices such as pedal input, brake usage, lane change frequency, vehicle start-stop frequency, etc. At the onset of a drive cycle, an initial state of mind of the driver may be predicted based on drive history (driver characteristics) as retrieved from the database. As such, there may be multiple states of mind of the driver and there may be a change in the state of mind during the drive cycle based on factors such as traffic and weather conditions. Each state of mind may correspond to a distinct PF regeneration factor which may directly influence the possibility of attaining a desired regeneration of PF over a given route. Responsive to an indication of a known destination (based on driver input) or a predicted destination (based on driving history and route forecasting algorithms) and further based on the current soot level of the PF and the initial driver state of mind, one or more routes may be selected from the database and hierarchically displayed to the vehicle driver. For a given destination, when the PF load is higher than a threshold and the driver is in a first state of mind, a first route may provide a higher PF regeneration efficiency while a second route may have a lower PF regeneration efficiency. But for the same destination, and the same higher than threshold PF load but a different driver state of mind, that first route may have a lower PF regeneration efficiency while the second route may have a higher PF regeneration efficiency. Navigational instructions may then be provided based on the driver selection. During the drive cycle, the state of mind may be updated in real-time based on driver interactions with traffic and environmental conditions such as weather. A non-homogeneous state transformation matrix may be used to determine changes in state of mind of the driver, during the drive. As the driver state of mind changes, the regeneration efficiencies of the routes may be recalibrated and an alternate route that now provides the highest PF regeneration efficiency may be displayed. The ranking of the selected routes may be adjusted in real-time based on the current driver state of mind such that the route at the top of the list may correspond to a highest degree of attainable PF regeneration.
In this way, by taking into account a current driver state of mind in selecting and ranking routes for regeneration of a particulate filter, the likelihood that a driver will follow the recommended route is increased. By estimating the driver state of mind in real-time based on driver interactions with traffic, and environmental conditions and updating the ranking of the displayed routes, a probability of attaining of a desired level of PF regeneration may be improved. By maintaining a database of frequently traveled routes with information including the actual degree of PF regeneration attained on each route and driver history on these routes, it may be possible to select one or more routes from a database based on PF regeneration requirement during a future drive cycle. The technical effect of using a non-homogeneous transition matrix to estimate changes in the driver state of mind during the drive cycle is that the constantly evolving traffic scenario may be optimally captured while determining the current driver state of mind, and its effect on the regeneration of the PF. By correlating each distinct driver state of mind to a regeneration factor, the influence of driver behavior on the regeneration may be quantified and accounted for during route planning and passive PF regeneration. In this way, by estimating suitable routes for PF regeneration while taking into consideration the influence of driver state of mind, regeneration of the system may be opportunistically carried out, thereby reducing over-loading of soot in the particulate filter and improving engine performance and particulate filter health.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.